Understanding customer behavior by mapping complaints to personality based on social media textual data
| Date | 09 September 2024 |
| Pages | 155-179 |
| DOI | https://doi.org/10.1108/DTA-02-2024-0162 |
| Published date | 09 September 2024 |
| Author | Andry Alamsyah,Fadiah Nadhila,Nabila Kalvina Izumi |
Understanding customer behavior
by mapping complaints to
personality based on social media
textual data
Andry Alamsyah, Fadiah Nadhila and Nabila Kalvina Izumi
Telkom University, Bandung, Indonesia
Abstract
Purpose –Technology serves as a key catalyst in shaping society and the economy, significantly altering
customer dynamics. Through a deep understanding of these evolving behaviors, a service can be tailored to
address each customer’s unique needs and personality. We introduce a strategy to integrate customer
complaints with their personality traits, enabling responses that resonate with the customer’s unique
personality.
Design/methodology/approach –We propose a strategy to incorporate customer complaints with their
personality traits, enabling responses that reflect the customer’s unique personality. Our approach is twofold:
firstly, we employ the customer complaints ontology (CCOntology) framework enforced with multi-class
classification based on a machine learning algorithm, to classify complaints. Secondly, we leverage the
personality measurement platform (PMP), powered by the big five personality model to predict customer’s
personalities. We develop the framework for the Indonesian language by extracting tweets containing
customer complaints directed towards Indonesia’s three biggest e-commerce services.
Findings –By mapping customer complaints and their personality type, we can identify specific personality
traits associated with customer dissatisfaction. Thus, personalizing how we offer the solution based on
specific characteristics.
Originality/value –The research enriches the state-of-the-art personalizing service research based on
captured customer behavior. Thus, our research fills the research gap in considering customer personalities.
We provide comprehensive insights by aligning customer feedback with corresponding personality traits
extracted from social media data. The result is a highly customized response mechanism attuned to individual
customer preferences and requirements.
Keywords Big five personality, Personality measurement platform, CCOntology, Machine learning,
Personalized customer service, Indonesia language, Tweet
Paper type Research paper
1. Introduction
The advanced industry era exemplifies the reciprocal influence of industrial experiences and
technological development (Castelo-Branco et al., 2022). Industrial development positively
affects financial status, enhancing economic growth (Panagariya, 2019). Nations can harness
the power of the digital revolution to enhance their financial and economic performance
(Gielens and Steenkamp, 2019). Information technology has notably emerged as the
predominant technology for improving business efficiency, paving the way for the
development of e-commerce (Onyancha, 2015;Bar
sauskas et al., 2008). Additionally, the
proliferation of mobile technologies, unlimited internet access, and the emergence of cloud
computing have significantly transformed the digital economy era (Dudhat and Agarwal,
2023). Social media platforms have become essential for marketing and customer
engagement (Zhao et al., 2020). With the ubiquity of mobile devices, customers have
unlimited access to online platforms (Hanaysha, 2022), facilitating the spread of information
among internet users (Dudhat and Agarwal, 2023;Greenberg, 2010). Companies increasingly
rely on social media to address customer complaints (Strauss and Hill, 2001;Prada
et al., 2022).
Data
Technologies and
Applications
155
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2514-9288.htm
Received 7 February 2024
Revised 14 June 2024
Accepted 17 August 2024
Data Technologies and
Applications
Vol. 59 No. 1, 2025
pp. 155-179
© Emerald Publishing Limited
2514-9288
DOI 10.1108/DTA-02-2024-0162
By utilizing large-scale data and implementing a data analytics approach, businesses
could provide personalized experiences, products, and services (Mudambi and Schuff, 2010).
Personalization has transformed the digital experience, offering tailored solutions that cater
to individuals’ unique needs and preferences. However, the effective implementation of
personalization faces challenges in accurately understanding individual needs, preferences,
and context due to the complexities of human behavior (Huang et al., 2019). This effort is
crucial in marketing, product recommendations, and user experience (Jiang and Liu, 2019;
Liu et al., 2019). Adapting to evolving preferences requires significant time and effort
(McKinsey). Personalization was hard to achieve in the past due to limitations in the data
source, knowledge extraction methodologies, and computation capabilities, but the big data
phenomenon offers promising solutions (Bender, 2020). By providing valuable insights into
individual behavior and preferences through statistical analysis and leveraging machine
learning algorithms, they enable more accurate recommendations and increased
personalization (Lopes et al., 2016). As a result, research focuses on personalizing the
customer experience in a business service context. It plays a crucial role in transforming
various sectors, including government services, communication, politics, finance, and
healthcare.
Customer behavior is crucial for businesses in the digital era. It refers to customers’
actions and decisions when buying goods or services, influenced by psychological and
emotional factors (Liu and Park, 2015). To increase sales, companies must conduct market
research and develop effective marketing strategies (Zhao et al., 2020). Positive customer
experiences lead to positive reviews and word-of-mouth recommendations, while negative
experiences result in negative feedback (Sun and Zhao, 2022;Li and Yang, 2012).
Understanding consumer behavior is critical for marketing success. Customers have
different personality traits when lodging complaints, which impact their interaction with the
company. Companies must acknowledge their customers’ personality characteristics and
tailor the interaction methods accordingly (Mccoll-Kennedy and Smith, 2006;Tronvoll, 2011;
Verhagen et al., 2013).
The big five personality theory (BFT) is a central psychological framework characterized
by five primary dimensions (Langford et al., 2017). It is extensively utilized alongside other
models such as the Myers-Briggs Type Indicator (MBTI), the Hexaco personality inventory,
Eysenck’s Personality Theory, Cattell’s 16 Personality Factors (16PF), and Motive-based
models. We choose BFT over these alternatives due to its robust empirical support and
widespread acceptance within the psychological community, making it a reliable tool for
predicting a range of life outcomes and behaviors. Its universalistic approach is supported by
cross-cultural research, indicating that the five dimensions can effectively describe
personalities across different cultures, thus enhancing its utility in diverse contexts.
Moreover, the simplicity and comprehensiveness of BFT make it highly accessible for both
academic research and practical applications in fields such as our study about customer
opinion. Extensive research has demonstrated BFT’s reliability and validity, making it a
robust framework (Wright and Jackson, 2023;Dhelim et al., 2021;Beck and Jackson, 2020).
One approach is to measure one’s personality based on textual expression on social media
(Dudija et al., 2022). We use a personality measurement platform (PMP) to classify people’s
personalities based on their posts on social media (Alamsyah et al., 2021). Nevertheless, it is
essential to acknowledge that BFT has limitations; it may occasionally reduce the
complexity of human personality to overly broad categories and could reflect cultural biases,
factors that must be considered when applying this model.
Most customers prefer filing complaints via social media as it provides an alternative
avenue to voice their concerns (Agostino and Sidorova, 2017). Social media is accessible,
allows for fast responses, and enables sharing experiences and receiving feedback (Strauss
and Hill, 2001;Sashi et al., 2019). Companies may utilize a CCOntology framework to analyze
DTA
59,1
156
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